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Modularized construction with precast concrete elements has many advantages, such as shorter construction times, higher quality, flexibility, and lower costs. These advantages are mainly due to its potential for prefabrication and series production. However, the production processes are still craftsmanship, and automation rarely occurs. Fundamental to the automation of production is digitization. In recent years, the manufacturing industry made significant progress through the intelligent networking of components, machines, and processes in the introduction of Industry 4.0. A key concept of Industry 4.0 is the digital twin, which represents both components and machines, thus creating a dynamic network in which the participants can communicate with each other. So far, BIM and digital twins in construction have focused mainly on the structure as a whole and do not consider feedback loops from production at the component level. This paper proposes a framework for a digital twin for the industrialized production of precast concrete elements in series production based on the asset administration shell (AAS) from the context of Industry 4.0. For this purpose, relevant production processes are identified, and their information requirements are derived. Data models and corresponding AAS for precast concrete parts will be created for the identified processes. The functionalities of the presented digital twin are demonstrated using the use case of quality control for a precast concrete wall element. The result shows how data can be exchanged with the digital twin and used for decision-making.
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Digital Twin Framework for Enabling
Serial Construction
Simon Kosse
*, Oliver Vogt
, Mario Wolf
, Markus König
and Detlef Gerhard
Chair for Computing in Engineering, Department of Civil and Environmental Engineering, Ruhr-University Bochum, Bochum,
Digital Engineering Chair, Department of Mechanical Engineering, Ruhr-University Bochum, Bochum, Germany
Modularized construction with precast concrete elements has many advantages, such as
shorter construction times, higher quality, exibility, and lower costs. These advantages
are mainly due to its potential for prefabrication and series production. However, the
production processes are still craftsmanship, and automation rarely occurs. Fundamental
to the automation of production is digitization. In recent years, the manufacturing industry
made signicant progress through the intelligent networking of components, machines,
and processes in the introduction of Industry 4.0. A key concept of Industry 4.0 is the digital
twin, which represents both components and machines, thus creating a dynamic network
in which the participants can communicate with each other. So far, BIM and digital twins in
construction have focused mainly on the structure as a whole and do not consider
feedback loops from production at the component level. This paper proposes a framework
for a digital twin for the industrialized production of precast concrete elements in series
production based on the asset administration shell (AAS) from the context of Industry 4.0.
For this purpose, relevant production processes are identied, and their information
requirements are derived. Data models and corresponding AAS for precast concrete
parts will be created for the identied processes. The functionalities of the presented digital
twin are demonstrated using the use case of quality control for a precast concrete wall
element. The result shows how data can be exchanged with the digital twin and used for
Keywords: digital twin, precast elements, industry 4.0, automation, internet of things
Prefabricated components offer advantages to the construction industry, such as low development
and manufacturing costs, simple repair and assembly processes, and increased exibility by allowing
the building to be adapted to new requirements through a different combination of components.
Especially in metropolitan areas, construction sites often signicantly impede urban dynamics. With
prefabricated components, the construction site area and construction time can be signicantly
reduced, thus minimizing the disruptive effects. Deconstruction or reconstruction is also easier and
faster with components and allows exibility and adaptability that cannot be guaranteed with
conventional construction methods. Modularized construction can also be described as serial
construction in which the components of the building are manufactured in series. This makes it
possible to produce buildings in shorter construction times, but also in higher quality and, due to the
economies of scale, in less expensive series production. A building is broken downinto as many
identical modular units as possible during factory planning. Accordingly, the aim of industrial
constructionis to advance the mechanization and partial automation of production processes in
Edited by:
Lavinia Chiara Tagliabue,
University of Turin, Italy
Reviewed by:
Pedro Mêda,
University of Porto, Portugal
Ibrahim Yitmen,
Jönköping University, Sweden
Simon Kosse
Specialty section:
This article was submitted to
Building Information Modelling (BIM),
a section of the journal
Frontiers in Built Environment
Received: 28 January 2022
Accepted: 16 March 2022
Published: 25 April 2022
Kosse S, Vogt O, Wolf M, König M and
Gerhard D (2022) Digital Twin
Framework for Enabling
Serial Construction.
Front. Built Environ. 8:864722.
doi: 10.3389/fbuil.2022.864722
Frontiers in Built Environment | April 2022 | Volume 8 | Article 8647221
published: 25 April 2022
doi: 10.3389/fbuil.2022.864722
factories. As a result, it is possible to increase productivity and
component quality in weatherproofed production conditions. All
in all, this leads to a high level of process reliability, predictable
planning effort, and easier cost control.
This holistic view of planning, production, and operating
processes has been redesigned and signicantly improved in
recent years through the introduction of Industry 4.0 methods.
Production in Industry 4.0 is interlinked with state-of-the-art
information and communication technology. Rigid and rmly
dened value chains are becoming exible and dynamic. Globally
networked value creation networks are emerging in digital
ecosystems that enable new forms of cooperation and at the
same time contribute to a climate-friendly and resource-
conserving future. Data spaces are emerging that guarantee
data sovereignty, security, and integrity and create the
conditions for innovative products and business models.
Industry 4.0 determines the entire life cycle of a product: from
the idea to development, production, use and maintenance to
The digital twin is described as a key element for Industry 4.0
applications. In the course of a products lifecycle, various
information sources, models, and tools become relevant.
However, the ow of information within or between the
individual lifecycle phases is currently often interrupted. For
example, information from the maintenance phase cannot
easily be fed back into the engineering phase to enable better
tuning of a devices parameters. This interrupted ow of
information not only leads to a dispersion or even loss of
information but also makes it difcult to access the right
information. A digital twin acts as a container for integrating
information from different sources at different lifecycle stages.
The information it contains can be in different formats, come
from different tools, and need not necessarily be stored in a
central location. A digital twin helps reduce the effort required to
access and manage information. The information contained in
the digital twin can also be used to analyze the current
performance of assets and derive design data for future systems.
In civil engineering, the focus of digital twins is often on the
description of entire buildings or infrastructures. The digital twin
is based on digital building models using Building Information
Modeling (BIM). Subsequently, these models are linked to real
data obtained with, for example, sensors. Aspects of the
production of individual components or elements are not
currently considered. In contrast, in the context of Industry
4.0, both components and machines are mapped using
individual digital twins, which in turn can communicate with
each other. The digital twins can change and expand in the course
of their life cycle. Ultimately, there is no single digital twin but a
dynamic network of twins. Even though the focus in this paper is
on the creation of AAS as digital twins for concrete elements, the
Reference architecture model Industrie 4.0 (RAMI4.0) as
described in (Deutsches Institut für Normung e.V., 2016)
denes an Industrie 4.0 componentas the combination of a
physical asset with its AAS. Those I4.0 components are tracked
over their life cycle and offer services on different layers. The
denition of I4.0 components is hierarchical in the sense of
granularity, meaning that from the smallest I4.0 component
up to the connected world,there is a strict hierarchy. In the
case of production in mechanical engineering, those granularity
levels are Product, Field Device, Control Device, Station, Work
Center, Enterprise, and Connected World. The transfer of these
concepts requires adjustment to the reality of civil engineering
and can utilize the previous work of Sacks et al. (2020) or Mêda
et al. (2021).
This paper shows how Industry 4.0 approaches can be
transferred, further developed, and used for the production of
precast elements in the construction industry. The concepts will
be explained and evaluated using selected examples. The
following research questions are to be answered:
How can digital twins be set up during production for
individual prefabricated elements?
How can the various information for planning, production,
and quality assurance be structured and dynamically linked?
How can the individual information on prefabricated
components be easily linked to form a digital twin for a
How can components and machines exchange information
and thus enable production control?
Following the introduction, State of the Art section gives an
overview of the current state of digital twins and their
applications in construction. In addition, Industry 4.0 and the
administration shell are presented as an implementation of the
digital twin in Industry 4.0. Concept section introduces the
concept, including an analysis of the underlying processes, a
description of the submodels of the administration shell, and an
introduction to the description of the interaction and
communication forms of the administration shell.
Implementation section uses the example of a precast concrete
wall to show how to create an administration shell for a specic
precast module and host it on a server. Finally, the results are
discussed in Discussion section, and conclusions are made in
Conclusion section.
The following chapter will give an overview of the concept of
digital twins and the current state of the art in the construction
industry. Furthermore, a brief introduction to Industry 4.0 and its
implementation of the digital twin, the Asset Administration
Shell, is given. Lastly, recent research that addresses the
adaptation of the administration shell in construction is
Digital Twin
The concept of the digital twin was rst described during a lecture
on Product Lifecycle Management (PLM)by Michael Grieves
(2016) at the University of Michigan. NASA adopted the concepts
for its next-generation aircrafts and published the rst denition
of a digital twin that is still widely used today: A digital twin is an
integrated multi-physics, multi-scale, probabilistic simulation of
an as-built vehicle or system that uses the best available physical
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Kosse et al. Digital Twin Enabling Serial Construction
models, sensor updates, eet history, etc., to represent the life of
the corresponding ying twin.(Tuegel et al., 2011;Glaessgen
and Stargel, 2012). Since then, many industries have adopted the
concept to their specic requirements, and various denitions
have emerged so that no common denition and understanding
of the concept exists. Some works are concerned with
characterizing the digital twin and distinguishing it from other
concepts such as a digital shadow or a digital model (Kritzinger
et al., 2018;Jones et al., 2020;Sepasgozar, 2021). Kritzinger et al.
(2018) categorize existing denitions and concepts of digital
twins in the literature based on their degree of integration.
They dene a digital twin as a virtual representation with an
automated bidirectional data ow with its respective physical
twin. In contrast, they dene a virtual representation with
automated unidirectional data ow as a digital shadow and a
digital representation without any form of automated data ow as
a digital model. One of the rst industries to exploit the potential
of this disruptive technology was the manufacturing industry. In
their work, Tao et al. (2018) describe a novel approach based on
the digital twin concept for product design, manufacturing, and
service, which aims to create a link between physical and virtual
products, ultimately leading to greater efciency, intelligence, and
sustainability. The construction industry is still in the early stages
of adapting the digital twin concept. In general, a digital twin in
construction is understood to be a virtual representation of
buildings, bridges, and other structures in the built
environment that goes beyond a pure 3D model and is similar
to denitions used in other industries. Boje et al. (2020) state that
Building Information Modeling (BIM) provides procedures,
technologies, and data schemas, while the digital twin conveys
a more holistic socio-technical and process-oriented
characterization by leveraging the synchronicity of cyber-
physical bidirectional data ow. Furthermore, BIM does not
have sufcient integration of control systems and sensor
networks for which dynamic data exchange is essential. Boje
et al. (2020) describe a digital twin consisting of three main
components: a physical component, a virtual representation, and
a data link connecting the two. To better understand the digital
twin in construction, Davila Delgado and Oyedele (2021)
analyze existing implementations of the digital twin of the
manufacturing industry. They compare digital twins, cyber-
physical systems, and BIM and categorize the implementations
based on their structural and functional descriptions.
Furthermore, the paper contributes to identifying key
concepts and process models for the implementation of
digital twins in construction. Shahzad et al. (2022) also
analyze the denition and characteristics of digital twins in
construction and highlight interactions with other digital
technologies and their applications and challenges in their
literature review. Akanmu et al. (2021) discuss the use of
digital twins in cyber-physical systems and propose use
cases for improving productivity, health and safety, life cycle
management of building systems, and employee competence,
among others. They identify key technologies and challenges
for implementing cyber-physical systems of the future in
Opoku et al. (2021) provide a comprehensive overview of the
current state of the digital twin. The number of publications
directly related to the digital twin application in the construction
industry has shown signicant growth since 2018. Opoku et al.
(2021) argue that the contextualization of the technology for
construction is completed. Most of the analyzed papers focus on
single phases of the lifecycle. Few concepts in the literature
consider multiple lifecycle phases. In construction, the
application of digital twins can be broken down into four
areas of the life cycle of a building structure: 1) planning, 2)
detailed design, 3) fabrication or construction, 4) operation and
Digital Twin for On-Site Construction
The use of digital twins on construction sites enables the
networking and exible provision of data for construction
machines, workers, and processes. Work in this area uses
Internet-of-Things methods and sensor networks to provide a
database for informed decision-making. In their work, Hasan
et al. (2021) discuss the operation of construction machines and
the automatic recording of construction progress. As part of their
work, they identify augmented reality and digital twins as key
technologies for implementing these systems. Turner et al. (2021)
identify industrial connectivity as a key technology to take
advantage of other technologies such as advanced data
analytics and articial intelligence, robotics, or sensors. In
addition, Turner et al. propose phased plans to implement
Industry 4.0 technologies. Liu et al. (2021) identify hoisting
building components on construction sites as the primary
source of hazards and propose a digital twin for safety
management. Their work contributes to improving the
efciency of information integration and dissemination, which
eventually leads to effective decision-making. In their work,
Zhang et al. (2022) propose a framework for using digital
twins to enhance the level of detail in BIM for construction
site management. For this purpose, they analyze and improve the
functionality of digital twins in terms of digital representation
based on BIM, the Internet of Things, data storage and analysis,
and interactions with the physical environment. Hou et al. (2021)
analyze the current state of integrating digital twin-based
technologies to improve workplace health and safety. They
conclude that the construction industry has not yet fully
realized the potential of digital twins.
Digital Twin for Building Lifcecycle Management and
Infrastructure Operations
For the operational phase, several concepts have been proposed in
the literature to improve the control and monitoring of a building
through the use of digital twins combined with computer vision
methods, sensor networks, and intelligent algorithms, ultimately
leading to a higher level of comfort and safety for the users of
intelligent structures. Lu et al. (2020) show that there are still few
efcient strategies to collect data from assets that can help
monitor, detect, record, and communicate operation and
maintenance problems. They present an approach that
combines the concept of a digital twin with advanced
computer vision and articial intelligence methods to detect
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Kosse et al. Digital Twin Enabling Serial Construction
anomalies in asset and building monitoring. Khajavi et al. (2019)
present a method for setting up a sensor network to create a
digital twin of a building. Furthermore, their paper discusses the
application of the digital twin to reduce costs and increase
comfort. Lydon et al. (2019) present simulation methods for
thermal systems integrated into lightweight roof structures using
a digital twin. Their approach uses models from the design phase,
among others, to support the operational phase by linking
operational data with design context data. Cognitive digital
twins can recognize complex behavior and dynamically
develop process optimization strategies and information-based
decision-making. In their work, Yitmen et al. (2021) investigate
the applicability, interoperability, and integrability of an adapted
model of cognitive digital twins in building life cycle
management, contributing to a better understanding of the
impact of integrating cognitive digital twins, machine learning,
cyber-physical systems, Big Data, articial intelligence and the
Internet of Things. Antonino et al. (2019) work augments BIM
models with advanced image recognition using real-time data
from camera systems to capture the occupancy of buildings.
Blockchain Technology and Digital Twins
Recent work is focused on the integration of blockchain technology
into digital twins. Götz et al. (2020) investigate the applicability,
interoperability, and integrability of a blockchain-based digital twin
over the entire lifecycle of an asset. They conduct a literature review
to identify use cases of the digital twin, blockchain technology, and
other technology from the Industry 4.0 context. Jiang et al. (2021)
use the unied standards and protocols of blockchain technology to
improve information sharing and communication to counteract
the fragmentation and discontinuity of information systems in
construction. They propose a blockchain-enabled cyber-physical
intelligent platform for modular integrated construction. Initial
results show that it improves the traceability of cyber-physical
construction progress, visualization, and evaluation of KPIs in real-
time, as well as reliability, immutability, and transparency.
Teisserenc and Sepasgozar (2021b) present a theoretical
framework for implementing an environmentally sustainable
blockchain-based digital twin for the construction industry. For
this, they analyze the life cycle of buildings and collectnecessarykey
data. Furthermore, they mention key factors and non-functional
requirements necessary for this kind of digital twin. In another
work Teisserenc and Sepasgozar (2021a), they propose an
implementation of blockchain technology for digital twins to
improve trust, security, efciency, information management, and
information sharing throughout the lifecycle of projects.
Digital Twin for Sustainability Assessment
In some research, the digital twin is recognized as an excellent
tool for data collection and analysis for sustainability assessments
of the built environment. Kaewunruen and Lian (2019) created
and analyzed a 6D BIM as a digital twin for the life cycle
management of railway turnout systems. The digital twin of
the turnout consists of schedule, cost, and sustainability data
throughout the life cycle. Their approach enables economic,
management, and sustainability assessment and shows that
material emissions during the manufacturing phase are the
most signicant contributor to the carbon footprint, and the
rebuild phase is the most expensive phase of the life cycle.
Tagliabue et al. (2021) propose a framework for moving from
a static to a digital twin and an Internet of Things-based dynamic
approach for sustainability assessment. The framework enables
real-time assessment and control of various user-centric
sustainability criteria. The presented methodology is the basis
for supporting decision-making processes related to sustainability
criteria throughout the life cycle.
Digital Twin in Prefabrication
Various works deal with the application of the digital twin
concept for prefabrication. Current work particularly addresses
accuracy in both design and fabrication of precast components.
Rausch et al. (2021) identify geometric conformance as essential
for the construction sites trouble-free assembly and structural
integrity of prefabricated components. They present a framework
for geometric digital twins based on 3D scans to detect assembly
and other potential problems that may arise. He et al. (2021)
propose in their work a BIM-based approach to support the
detailed design and digital fabrication. The developed tool can
create more accurate geometric models for production.
Simulation of the tool path in 3D printing demonstrates its
applicability to digital manufacturing. The research of Tran
et al. (2021) is concerned with the quality assurance of
nished parts. Tran et al. developed a digital twin-based
approach to capture the geometric quality of prefabricated
facade elements. The framework performs automated
matching between 3D as-designed and 3D as-built data based
on a LIDAR point cloud. Some works address digital twin-based
precast supply chain improvement. Zhong et al. (2017) present a
platform that combines Internet-of-Things with BIM approaches
to provide real-time visibility, traceability, and advanced
decision-making throughout the process. Lee and Lee (2021)
developed a digital twin framework based on building data
modeling, IoT sensors, and GIS to enable real-time logistics
monitoring and simulation of supply chain processes in
precast construction.
Industry 4.0 Approaches
Industry 4.0 is characterized by the infusion of information and
communication technology innovations into the industrys
products, services, production processes, and organizational
structures (Monostori et al., 2016)(Monostori, 2014). The
characteristics of Smart Products (SP) were dened by
committees such as the International Academy of Production
Engineering (CIRP) (Abramovici and Stark, 2013), the German
Electrical and Electronic ManufacturersAssociation (ZVEI -
Zentralverband Elektrotechnik und Elektronikindustrie e. V.,
2016), and the American Initiative IIC (Industrial Internet
Consortium). Likewise, Cyber-Physical Production Systems
(CPPS) open new opportunities for a paradigm shift in
production towards a exible lot size one production (Bif
et al., 2017).
International standards for interaction models and
architectures also provide frameworks for standardizing the
global movements of research projects in the eld of
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Kosse et al. Digital Twin Enabling Serial Construction
digitization (Monostori, 2014). Examples include the German
reference architecture model industry 4.0 (RAMI 4.0) or the
American IIRA (Industrial Internet Reference Architecture).
The introduction of a uniform digital representation for each
type of (smart) product is a necessity for the desired level of
interoperability in Industry 4.0. This can be accomplished using
the asset administration shell model from the Reference
Architecture Model Industry 4.0 (RAMI 4.0) (Plattform
Industrie 4.0, 2016;Deutsches Institut für Normung e.V.,
2016). In RAMI 4.0, each so-called asset is developed, created,
used, and disposed of, and is thus subject to a life cycle that
follows industry standards. Regardless of whether it is a physical
product, a software component, or a service, an asset is a tangible
or intangible entity with value for the business. The data
describing an asset is digitally stored in the accompanying
asset administration shell (AAS) and made available for
human-machine and machine-machine communication,
resulting in an Industry 4.0 component as the xed
combination of an asset and its AAS (Plattform Industrie 4.0,
2016). By combining I4.0 components, a new I4.0 component is
created in each case: a new asset with its AAS. This creates a
recursive asset description that can reach any level of granularity,
from simple semi-nished products to manufacturing plants. The
concept of I4.0 components serves as the basis for introducing
Industry 4.0 based on smart products (Plattform Industrie 4.0,
2017;Roth, 2016). To design smart products, current research
efforts are moving towards new holistic development methods,
such as smart engineering (Abramovici, 2017) or smart systems
engineering (Gausemeier, 2014). The literature review by
Kritzinger et al. (2018) highlights the different understandings,
denitions, and concepts surrounding digital representations.
The Digital Twin in the context of Industry 4.0 is a fully
integrated digital depiction of a physical product, in which
changes in the information world cause changes in the
physical world and vice versa (Kritzinger et al., 2018). This
fully integrated digital representation is referred to as the AAS
in RAMI 4.0.
The Asset Administration Shell
The structure of the AAS is based on a technology-neutral
metamodel specied using the Unied Modeling Language
(UML). It consists of a header in which information on
identication and a designation of the asset are stored and a
body in which data and information on various aspects and use
cases are stored in a large number of independent submodels. As
a result, the AAS is a modular structure of submodels that
together comprise the digital representation of the asset rather
than a single, all-encompassing model. Each submodel has its
semantic description as well as a distinct identication. The
separate denition of submodels offers several advantages,
including the ability to generate content in parallel and to
continuously grow, as well as the fact that it is reusable. A
distinction is made between the type and instances of assets,
submodels, or features. The data and information for a
component family are stored in a type AAS. At the start of
production, component-specic data is stored in an instance AAS
(Boss et al., 2019;Boss et al., 2020). To ensure a standardized
exchange of information throughout the lifecycle, the goal is to
create a standardized template for each submodel (Plattform
Industrie 4.0, 2020a).
AASs exist in three different implementation forms, which
differ in their data exchange and communication capabilities.
Type 1 is a passive AAS in le format enabling standardized
information exchange across life cycle phases. Direct
communication is not possible. Type 2 is a server-based AAS
that provides its contents via an API. Similar to type 2, type 3 is a
server-based AAS but, unlike type 2, can actively and
independently initiate interactions with other I4.0 components
for data and information exchange. The data exchange or
communication between the components is implemented via a
specic I4.0 language (Plattform Industrie 4.0, 2021b). Different
data formats are required in communication protocols or
information models depending on the lifecycle phase and
application. In order to ensure interoperability as well as data
exchange functionality over the entire life cycle, schemas for
serialization in XML and JSON format exist. Mappings to
standard data formats such as OPC UA, RDF, or
AutomationML are also possible. For information exchange
with ofine AASs, there is also a package format that, in
addition to serializing the AAS in XML or JSON format, also
contains the content referenced in the AAS, such as PDF-les
(Plattform Industrie 4.0, 2020a;Plattform Industrie 4.0, 2021b).
Properties, element collections, or les are examples of submodel
elements that describe the state or behavior of an asset. Assets can
have dened capabilities, allowing for asset searches based on
functions (Plattform Industrie 4.0, 2020a). Existing standards,
such as ECLASS, can be referenced by a SemanticID (Plattform
Industrie 4.0, 2020b).
In addition to the ofcical specication of the AAS, various
other publications exist adressing the AAS. The terms, concepts,
and the vision of the AAS and the Digital Twin are compared
along a plants lifecycle resulting in a holistic version of a Digital
Twin that equals an AAS (Wagner et al., 2017). To better
understand the AAS, Ye and Hong (2019) created an AAS of
a manufacturing line for demonstration purposes. The authors
also describe the AAS benets and provide implementation
recommendations. The potentials of implementing the AAS
with a given standard for cyber-physical systems are presented
together with different possible business models and
communication technologies (Tantik and Anderl, 2017).
Many publications are focusing on integrating existing
standards into the AAS. Grangel-Gonzales et al. (2016)
showed that structured information in the AAS based on
RDF can be easily accessed with URI-based identication to
achieve loss-free communication and interoperability. This
simplies the integration of existing standards like eClass.
Focusing on the communication technologies, a rst
framework for describing interactions and exchanging
information exchange between multiple AASs with the
oneM2M standard is implemented by Willner et al. (2017).
To increase the interoperability, Fuchs et al. (2019) showed how
a semantic description of an asset based-on an OPC UA
information model could be translated into the AAS
information model without losing information. Other
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Kosse et al. Digital Twin Enabling Serial Construction
publications are focusing on implementations using the AAS
concept. Toro et al. (2020) show how AASs can be implemented
in mini-factories and proved their concept with a case study
demonstrating the possible capabilities of Industry 4.0. A
capability-based engineering methodology based on AAS was
introduced by Huang et al. (2021). The methodology consists of
the phases, capability checking, feasibility checking, and the
nal skill execution. Also, in the context of capability-based
production, Motsch et al. (2021) presented a prototypical
implementation of an energy-related submodel with BaSyx-
Asset Administration Shell in Construction
The rst research works deal with the adaptation of the
administration shell in the construction industry. Wolf et al.
(2021) present the rst approach to applying the administration
shell as a digital twin and thus extend current BIM approaches by
a cyber-physical component. Their work describes requirements,
contents, and interactions based on the use case of quality control
for precast concrete parts. Similar to other areas of civil
engineering, the information management of bridges is
fragmented and discontinuous in the life phases of planning,
construction supervision, and execution. Braml et al. (2022)
developed a digital twin based on the AAS, which focuses on
the physical engineering model. The digital twin captures the
bridge structure holistically over their entire lifetime and provides
a data basis for AI-based analyses.
Digital twins can fundamentally improve the design, automated
production, installation, and maintenance of building
components in the construction industry. Current concepts
tend to follow central and not very exible modeling, storage,
and exchange of relevant information for the life cycle of a
component. This paper presents a decentralized and extensible
digital twin for the industrialization of precast concrete
production applying Industry 4.0 approaches. If end-to-end
and interlinked digital models are available, precast production
can be better controlled and made more efcient. Together with
other Industry 4.0 components, cyber-physical production
systems (CPPS) can be built to support the automation in the
industry construction. Various information is already provided
via Building Information Modeling approaches. Therefore,
suitable approaches for linking Industry 4.0 and Building
Information Modeling must also be developed. This paper
focuses on the fundamental concepts to build digital twins to
introduce CPPS for the production of precast concrete elements.
The following aspects are analyzed and elaborated in detail (cf.
Figure 1):
Identication of relevant production processes and
associated information requirements for precast concrete
Integration of existing and development of additional data
models for the identied processes as well as their Industry
4.0 compliant description
Elaboration and support of the communication between
precast elements and machines for the establishment of
For the development of digital twins for the production of
precast elements, it is essential to consider the whole production
system, including the machines and processes. The production of
precast concrete elements generally includes forming,
reinforcing, concreting, stripping and curing, and quality
assurance. All relevant data and the status of the precast part
must be continuously recorded, compiled, and made available for
these processes.
An essential requirement for a digital twin is a connection to
its physical counterpart, which enables communication to
FIGURE 1 | Schematic illustration of the overall concept.
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exchange data and information and access to the functions of the
digital twin. Data from sensors, control commands, and machine
parameters are transmitted for automatic production. For some
use cases, data exchange must take place in real-time. Therefore,
the data structure must be extensible to allow new data and
information to be added throughout the lifecycle.
The digital twin of a precast concrete component contains
information about the component (e.g., material or dimensions)
as well as information about the production process or required
qualities. In this way, the digital twin can, for example, initiate
contact with the reinforcement bar bending and welding machine
and transmit all the necessary machine parameters. During the
production of the reinforcement, the machines digital twin
transmits the status of the process step in real-time and, when
the process step is completed, transmits a log back to the digital
twin of the precast element as manufacturing feedback. In
addition, the digital twin of the precast element knows its
position in the building structure and neighboring components.
The concept of the AAS of Industry 4.0 is used to build the
digital twin. General structures for identifying and describing
precast concrete elements can be adopted directly. However, AAS
only provides a framework that must be structured in a
problemspecic manner. Therefore, the relevant submodels
must be identied and specied for precast concrete elements.
Furthermore, approaches for the interaction with other digital
twins, respectively AAS components, must be developed. The
focus of this paper lies mainly on the submodels for the support of
the production processes with an emphasis on quality assurance.
For example, a submodel modeling the aspect of quality
assurance would have to enable a target/actual comparison
between design and measured data in the context of
production. Different data structures are required for such
questions. However, the submodels must be linked
semantically to enable a uniform query.
Process Analysis
This paper considers the individual steps required to produce
precast concrete elements in a ow manufacturing process.
the digital twin in the form of the administration shell and
its submodels. The starting point for the analysis is the
completed detailed design of a precast concrete part.
Information about the dimensions, material, and quality
specication, as well as tolerances and fabrication
instructions, are provided by corresponding submodels and
external data sources (e.g., BIM models). In addition,
all relevant data and information must be modeled for each
production step, including the construction of the formwork,
insertion of the reinforcement, concreting, and removal of
formwork from the nished element (cf. Figure 2). In order to
keep formwork times as low as possible, it is sensible in a ow
manufacturing process to additionally cure elements using
heat treatment. After each completed production step,
quality control is carried out, for example, by visual
procedures that check the position of the reinforcement and
the formwork to ensure quality. At the end of the
manufacturing process, the nominal and actual dimensions
of the components are checked and compared with tolerances
in nal quality control.
For the automation of the production processes, the data and
information must be made available. Furthermore, both data
transmitted back from the production processes and the results of
the quality controls must be stored and made available for further
analyses. Submodels are dened below for essential use cases.
Other submodels can be created analogously to this procedure
and added to the administration shell.
Submodels have already been developed and standardized for
some use cases. These include submodels for identifying an asset,
providing technical data and information, and documentation. It
is sensible to base administration shell developments on
standards and add use case-specic submodels. The submodels
described below are a sound basis for developing administration
shells. They are also consistent with the concepts and principles
dened in DIN EN 17412-1 (Deutsches Institut für Normung
e.V., 2021a) for determining the level of information need, which
is described in the sense of geometric and alphanumeric
information and documentation. Different applications have
different data needs reected in the administration shell
through different submodels.
Submodel Identication: The Identication submodel was
developed for the unique identication of an asset and for
retrieving the asset via its characteristics (Plattform Industrie
4.0, 2020b). The prerequisite for communication between two
I4.0 components is the unique identication of both components.
For this purpose, the submodel contains machine-readable
features that serve the identication. The unique machine-
readable features would rst be exchanged to initiate
communication before querying additional human-readable
features in the intended use case.
Submodel Technical Data: The submodel Technical Data
provides an interoperable asset description based on technical
properties. In the intended use case, the submodel describes an
FIGURE 2 | Production and quality control steps for precast concrete elements.
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asset based on technical data that other actors can clearly
understand, such as market participants or operators of
industrial plants (Plattform Industrie 4.0, 2020b). The
submodel provides a base set of submodel elements that
provide the required information for this use case. For the
application in the construction industry, the submodel has
general information about the manufacturer and technical data
such as main dimensions, exposure classes, or reinforcement
volume ratio of the component.
Submodel Documentation: The Documentation submodel
developed for the process industry was developed for the
interoperable provision of information from manufacturers to
operators of industrial plants (Plattform Industrie 4.0, 2020b).
The submodel denes standardized metadata and classication
for documents. The stored documents include, for example,
construction, installation, or maintenance instructions. The
intended use case in construction includes technical
documents such as reinforcement and formwork plans,
transport and assembly instructions, and delivery bills.
The following chapters will discuss unique and new submodels
of precast concrete elements, including general information of the
detailed design such as dimensions, material, or BIM models.
Furthermore, the submodels for production and nal quality
control will be addressed.
For the automation of the production process with the
proposed methodology, other assets also need to be
represented by digital twins or AAS, including, e.g., machines
or other reusable resources such as formwork elements. The AAS
of a production system is outside the scope of this paper. Crucial
for realizing a cyber-physical production system is a
communication among the participants. Therefore, at the end
of this chapter, communication possibilities and paths relevant
for precast concrete elements are described.
Domain experts can only create the concrete contents of
the submodels. Submodels should be continuously reviewed,
updated, and, if possible, standardized. Established norms and
standards provide a sound basis for the development of
submodels. For precast concrete elements, these are
essentially DIN EN 206 (Deutsches Institut für Normung
e.V., 2021b), which species requirements for concrete,
DIN 488 (Deutsches Institut für Normung e.V., 2009a)
(Deutsches Institut für Normung e.V., 2009b), which
species requirements for reinforcement steel and DIN EN
13369 (Deutsches Institut für Normung e.V., 2018), which
species general requirements for precast concrete elements,
as well as various product standards that contain the specic
requirements for different precast concrete element types.
Furthermore, similar to submodels, ontologies represent
the knowledge of a specic domain. Ontologies are
therefore also an excellent basis for creating submodels
for AAS.
Submodel Detailed Design
Today, Building Information Modeling offers many advantages
for the planning of precast concrete elements. Detailed models,
for example, also provide added value in the bidding phase
through simple quantity takeoff. Thanks to intuitive tools, any
structure can be modeled with the highest level of detail and the
data can be exchanged based on open standards (Industry
Foundation Classes). BIM models help all parties involved
understand design and planning and improve collaboration
between the planning and design ofce, production, and the
construction site.
Generally, a distinction must be made between geometric and
semantic information for detailed design. For the submodel, the
geometry of the entire precast concrete element, including the
reinforcement, is mapped based on the Industry Foundation
Classes (IFC). The Detailed Design submodel contains a link
to an IFC le that includes all the information of the geometry.
GUIDs (Globally Unique Identiers) of the individual IFC objects
(e.g., complete precast element, individual reinforcement
elements) are used for this connection. In the Detailed Design
submodel, the geometry is only stored in a simplied form by
means of general dimensions (e.g. width, height, and depth). The
specic geometry must be determined via the IFC le.
Semantic information, especially about the material, is
already stored in IFC models. However, there is no
comprehensive standardization of individual material
parameters. Although there is an initial standardization
through IFC4Precast, many material parameters are still not
included. Therefore, storing the material parameters directly in
the Detailed Design submodel is sensible. For this purpose, a
separate ontology has been developed. The ontology thus
includes different information besides the concrete type, such
as exposure class, design class, or chloride class. Furthermore,
information on the cement content includes a detailed
description of the cement used.
In addition to these properties, information on the mechanical
properties of the material is also stored. In the previous chapter, it
was mentioned that details about the reinforcement also have to
be stored in the asset administration shell. This information is
also assigned to this submodel. Consequently, the submodel must
include several element collections. The rst collection contains
the details on the composition and properties of the material and
the components described above. In another collection, the
reinforcement is described in detail. Mechanical properties and
geometric characteristics are recorded as well as information on
strength classes or reinforcement types. It is also possible to insert
a reference to eClass, where many properties are precisely
The Digital Construction - Building Materials Ontology
(dicbm) and The Building Material Ontology(bmat) are
suitable as a basis for the development of material information
for this submodel (Karlapudi and Valluru, 2019;Hamdan, 2021).
Both ontologies dene the necessary relationships between
components, materials, and their respective properties.
Figure 3 shows the metamodel of the ontology created by
combining both ontologies. The aspects of quality and delivery
are not addressed by either ontology but are important for
manufacturing precast elements with ow manufacturing
methods and are therefore added.
ThemainclassoftheontologyisBuilding Material from the
bmat ontology, which represents a building material and can
be assigned to a building element via the object property has
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Kosse et al. Digital Twin Enabling Serial Construction
Building Material. The representation of a building element is
done by the Building Element Ontology (Pauwels), which can
be used to dene common building elements based on the IFC
class Ifc Building Element.Furthermore,theMaterial Type and
Property classes of the dicbm ontology are children of the main
class. The Material Type class allows a classication of the
material into different material types, and the Property class is
used to dene the properties of the materials. Delivery
Information and Quality classes are added to represent the
aspects of delivery information and material quality.
Submodel Quality Control
The purpose of the Quality Control submodel is the interoperable
provision of information required to ensure the quality of precast
concrete elements. The focushere is particularly onthe provision of
quality requirements based on standards and regulations,
machine parameters for quality assurance, and
measurement and evaluation results.
The intended use case is that the quality assurance process is
unambiguous and understood for the used devices and machines.
In this submodel, the quality requirements for the tolerances of
the produced precast element are compiled and made available
for evaluation. All data for the conguration of the devices and
machines can be retrieved and transferred. In addition, the results
recorded during the process are stored for evaluation considering
the specied tolerances. The evaluation result is documented
accordingly and is the basis for further decisions.
The Construction Quality Inspection and Evaluation
Ontology (CQIE) developed by Zhong et al. (2012) is suitable
for developing the contents of the Quality Control submodel. The
CQIE ontology was developed to dene the essential concepts and
relationships in construction quality inspection. In the context of
this work, the ontology was extended to include the aspect of
equipment and machinery used. Figure 4 shows the augmented
metamodel of the CQIE ontology based on Zhong et al. (2012).
The ontology consists of the main class Inspection-Task,
subordinated by the Inspection-Object, Inspection-Checking-
Action,Evaluation-Task, and Regulation-Constraint classes.
Through the Inspection-Object class, all entities are dened
that are regulated by regulations and must undergo a quality
assurance procedure. The quality data and information to be
collected are compiled in the Inspection-Checking-Action class
and have a Checking-Result that represents the actual
inspection result. The result can be recorded, for example,
in the form of length, angle, and atness deviations. These
deviations originate from the evaluation of the target/actual
FIGURE 3 | Metamodel for the Ontology of the Material Submodel based on the Building Material Ontology (bmat) and Digital Construction - BuildingMaterials
Ontology (dicbm).
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Kosse et al. Digital Twin Enabling Serial Construction
comparison of the component, which, in the case of a laser
scan, for example, is done by superimposing the point cloud
with the geometry of the component stored as a BIM-Model.
To compare the inspection results with the regulations, the
Inspection-Task class has the Evaluation-Task class,
consisting of Evaluation-Criteria and Evaluation-Result,
which model the comparison and the result of the
comparison, respectively. The Regulation-Constraint class
represents the regulations and standards that contain the
corresponding tolerances and requirements. For example, the
tolerance is shown here, analogous to the length, angle, and
atness deviations. In addition, all required parameters, such
as machine or geometric parameters, are represented by the
Parameters class. The devices and machines used, including
laser or CT scanners, are represented by the Inspection-
Device class, extending the CQIE ontology. Parameters
necessary for the conguration of the devices are assigned
to this class.
Submodel Production
The purpose of the Production submodel is the interoperable
provision of information required for production and interaction
within the production system. The focus here is on
the provision of parameters for the production machines,
the logging of production processes and live data
monitoring, and
the compilation of the required equipment and resources.
The intended use case is that the process steps are described
unambiguously and comprehensibly for all elements of the
production system as well as the users so that the necessary
process steps can be initiated, congured, and controlled through
the exchange of information. Data needed to optimize production
is collected in this submodel and made available for evaluation.
Although several ontologies can be found in the literature that
model production systems also concerning Industry 4.0, none
met the specic requirements for modeling the production of
precast concrete elements. Instead, an ontology was developed for
the Production submodel, whose metamodel is shown in
Figure 5. The ontology presented is intended to dene the
general concepts and relationships associated with precast
concrete products, including production steps, equipment, and
machine parameters.
The main class of this ontology is the Production-Task class
and represents the entire production process for which data
concerning the status and date of production are collected.
Subordinate to this class are other classes, Building-Element,
Item-Production-Action, and Production-Process. The class
Building Element originates from the Building Element
Ontology (Pauwels, 2020) and denes all common building
elements based on the IFC subtree Ifc Building Element. The
Item-Production-Action class compiles the production steps
required to produce the nished part. The Production-Result
class represents the result of the production steps. The
production processes are dened by the Production-Process
class, which in turn consists of
FIGURE 4 | Metamodel for the Ontology of the Quality Control Submodel based on Zhong et al. (2012).
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Kosse et al. Digital Twin Enabling Serial Construction
the Production-Machine class, which represents the
required production machines and provides machine
parameters for them,
the Production-Equipment class, which represents the
equipment and resources required for production,
including formwork and reinforcement, and
the Process-Data class, which provides all live data that can
be retrieved during production.
Production processes include reinforcement, forming,
concreting, and curing. Data regarding the start and end times
are collected for evaluation of each of these processes.
In order to enable a largely self-organized and self-controlled
production, communication and interaction possibilities must be
available for data exchange with humans, machines, and other
precast elements represented by AAS. For this purpose,
communication options should be dened to retrieve, modify
and delete certain contents of the submodels. The
communication possibilities can be divided into machine-to-
human (m2h) and machine-to-machine (m2m)
communication and further subdivided into horizontal and
vertical communication. For a better understanding, the
possible communication types are explained based on the
production processes presented in Process Analysis section,
namely curing and quality control (cf. Figure 6).
Machine-To-Machine Communication
The machine-to-machine communication takes place in the
virtual world. It is the horizontal communication between two
or more AAS. In the highlighted production process, several
possibilities for horizontal communication exists. The AAS of
the precast concrete element (AAS CE) can communicate with
the AAS of the heat treatment system (AAS HT) or with the
AAS of the quality control system based on laser scanning
(AAS QC). The submodel Production is relevant for the
communication between the AAS HT and AAS CE. In this
submodel, all necessary data for the heat treatment are stored.
Therefore, this information is sent from the AAS CE to the
AAS HT. In the other direction, a log le is sent after
completion of the heat treatment, which is then stored in
the Production submodel of the AAS CE. The preceding
process steps run according to the same principle. For
concreting, for example, the data from the Detailed Design
submodel are transmitted together with the process
FIGURE 5 | Metamodel for the ontology of the production submodel.
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parameters from the Production submodel. Not all data stored
in the AAS CE are transmitted, just the data required for the
specic production step.
The communication between the AAS CE and the AAS QC is
similar, but here several submodels are relevant in the AAS CE.
First of all, the required process parameters are stored in the
submodel Quality Control. These are sent to the AAS QC. After
the execution, in this case, for example, a laser scan, the
inspection date, and a point cloud are sent back to the AAS
CE. The target and actual values must be accessed to compare
measured and planned values. For this purpose, the data from
the AAS CE are obtained. In particular, on the one hand, the
actual data from the Quality Control submodel where the
permissible tolerances that are also required are located, and,
on the other hand, from detailed design. Thus, the target data in
the form of a 3D le are retrieved from a service and evaluated.
The results are then stored in the Quality Control submodel in
the form of a status describing the modules state. Consequently,
the m2m-communication in the virtual world can be used to
exchange data between two or more AASs, or it can be used by a
service that accesses the data from AASsandprocessesthemas
Besides m2m-communication in the virtual world, it is also
possible to communicate between the virtual and the real world.
The communication can be explained here by the AAS QC and
the physical quality control system. The AAS QC has received
the required settings for quality control from the AAS CE. These
are now transmitted to the system to perform quality control.
Afterward, the AAS CQ receives the data from the quality
control and can forward them to the AAS CE as
described above.
The communication between the precast concrete element
and its AAS is different in comparison. First of all, a simple
concrete element is not able to communicate. This can change
if, for example, sensors are installed in the element. These
elements then provide information about specicvalues.Each
precast element gets its own ID-Tag (e.g., barcode, RFID). The
tag is linked to the AAS CE, therefore, scanning the tag will
show the information stored in the elements AAS. However,
there is no direct communication between the concrete
element and its AAS. All information regarding the status
of the concrete element comes from systems and their AASs
involved in the production process.
Human-To-Machine Communication
Lastly, in addition to machine-to-machine communication,
human-to-machine communication is possible. To monitor
production, the content of all AAS must be retrievable and
essential information must be displayed (cf. Figure 6).
Dashboards can be used to visualize key performance
indicators (KPIs) and the production status of the concrete
element. With the dashboard, the user can read data, send
commands to the AAS, and overwrite process parameters
stored in the AAS.
The communication possibilities shown above indicate that
implementing the presented concept with the AAS would offer
a lot of potential for fully digitalized and automated
A prerequisite for applying the proposed submodels in their
domain is an appropriate source for data and information.
Standards, guidelines, and other digital models are preferred
sources for the needed data, whereas BIM models are
particularly suitable for their semantic information about each
modeled component. An extension of the approach by other
FIGURE 6 | Communication scheme using the example of the heat treatment and quality control process steps.
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Kosse et al. Digital Twin Enabling Serial Construction
sources, such as eClass, is planned for future work. As a
demonstration scenario, the focus is on the quality control
after production of a precast concrete wall element, depicted
in Figure 7.
The example precast wall element is 5 m wide, 4 m high, and
0.2 m deep. At the height of 1.5 m and a distance of 0.9 m from
the left edge of the wall, there is a 1.20 m high and 0.68 m wide
window opening. The digital model of the component, which is
available in IFC data format, contains semantic information
regarding material, reinforcement, and geometry. The
proposed steps for creating AAS consist of templating,
instantiation, nalization, and deployment. After that, a web-
based application can access, update, and validate the quality
control data.
The authorsgeneral template for an AAS of a precast concrete
element consists of the standard submodels used for
identication and the proposed construction-specic
submodels, which describe all relevant characteristics of the
precast concrete elements. Although the general submodel
structure is the same for all concrete elements in a precast
process, the data structures, and contents of the submodels are
not necessarily identical. The characteristics of the submodels
vary, for example, depending on the materials used or the
layout of reinforcement selected. In addition, the submodels
describing the production and quality assessment process vary
according to the process steps and technology used. The
resulting AAS templates for specic detailed designs are
stored on a server in a database or repository so that, due
to the semantic searchability of the AASs, components can be
found based on the properties and characteristics dened in
the AAS. The AASX Package Explorer is used to create the AAS
templates. The AASX Package Explorer is a software tool with
a graphical user interface designed for modeling and
manipulating AAS, with export options in XML and JSON
formatsandthenativeAASXformat(Plattform Industrie 4.0,
The templates developed in the previous step are loaded into the
AASX Package Explorer to add all required components or
instance-specic data. Data needed for the instantiation can be
taken from external sources, which enables the automation of the
instantiation process. The authors implemented a Java-based
software tool for the automated instantiation of the AAS based
on data and information from an IFC le and using the
aforementioned templates.
In the scenario at hand, the implementation is done using off-the-
shelf components of the BaSyx middleware as type 2 AASs with
API-based access. With this server implementation, the AAS and
its submodels are made available and accessible. The standard
Rest-API can now be used to query, change, and add AAS,
submodels, or individual propertiesvalues. For example, the
Quality Control submodel can be queried via a GET request as
depicted in Figure 8.
The content of the submodel is then returned as the
response. A PUT request can then be used to add or change
submodels, properties, or set values for a specic property
(Ziesche, 2021).Thefollowingusecasesmakeuseofthisserver
Case Study 1: Detailed Design Submodel
The rst part of the proof of concept is the instantiated submodel
for the detailed design of the aforementioned concrete wall
element. Figure 9 shows the Detailed Design submodel based
on the requirements and content described in the concept by
ontologies. The submodel essentially consists of the two areas,
Dimensions and Material, represented by submodel element
collections (SMC). In addition, the submodel contains a
reference to an IFC le of the component.
The Dimensions section contains general dimensions of the part
that dene a virtual bounding box by the characteristics of width,
height, and depth around the part.
The Material section contains data and information on each
material used in the component. The area is subdivided into two
additional SMCs representing Concrete and Reinforcement Steel
in the present case. The development of further SMCs to
represent new materials is the scope of future work.
The Concrete SMC contains basic information regarding the
concrete type, strength class, classication into exposure,
chloride, design, and consistency classes. Furthermore,
information and quantities on the cement, aggregate,
additives, and admixtures used can be found. Information
on mechanical properties and delivery information are also
included. The used cement and aggregate information are
stored in one SMC each. The cement used is described by
type, strength class, and information about the supplier of the
cement. The information on the aggregate includes various
FIGURE 7 | BIM model of the precast concrete wall element.
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Kosse et al. Digital Twin Enabling Serial Construction
properties for classifying the aggregate, such as grading curve
or platitude index, and information on the supplier.
The Reinforcement Steel SMC has product form and standard
information, ductility class, and design class. Mechanical,
welding, and delivery information are listed in separate SMCs.
For example, the mechanical properties include data on yield
strength, modulus of elasticity, and density of the steel. The SMC
relating to welding properties gives information on the
proportions of carbon, phosphorus, nitrogen, sulfur, or copper,
details essential for weldability. Delivery information is given
analogously to the SMC already described for concrete.
The submodel shown in Figure 9 contains data and
information for a C30/37 concrete and B500A rebar. All
dened properties have a concept description. The right part
of the gure shows an example of the feature Cube Compressive
Strength, which has a unique ID and Concept Description. The
concept description includes a denition and denition source of
the characteristic, an indication of the physical unit, and a
formula symbol. Alternatively, a reference to an external
repository such as eClass could also be made here.
Case Study 2: Final Quality Control Process
Figure 10 shows the submodel for quality control. It consists of
Process,Requirements, and Results, each represented by an SMC.
In addition, the submodel contains the Status feature, which
represents the evaluation result of the quality control. The
required tolerance values were taken from the DIN 18203-1
(Deutsches Institut für Normung e.V., 1997) standard, which
has been withdrawn as such but still represents the current state
of the art and is used in practice.
The SMC Process contains essentially all the parameters required
for conguring the quality assurance system and executing the
FIGURE 8 | Structure of the URL to access a specic submodel.
FIGURE 9 | Detailed Design Submodel.
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Kosse et al. Digital Twin Enabling Serial Construction
process. In the context of the presented use case, all required
parameters for a laser scan are provided in another SMC Laser
Scanning, which in turn is subdivided into further SMCs
System Requirements, which lists technical requirements for
the quality assurance system,
Laser Scanner, which contains information about the
selected scanner,
Legs, which contains the position and congurations for the
individual measurement runs, and
Advanced, which enables advanced congurations of the
laser scanner.
The InspectionDate feature documents the inspection date.
The SMC Requirements contain requirements placed on the
component and must be veried by the quality assurance
process. In this case study, a virtual laser scan is performed to
verify the parts dimensions. Included are features that represent
the width, height, and depth tolerances and tolerances for atness
and angular deviations. The tolerances were taken from the
relevant standards and regulations. Additional requirements
can be added for other quality assurance procedures, such as
CT scans.
The SMC Results contain the measurement results of the quality
assurance procedure. Analogous to SMC Requirements, the
collection contains measured length, width, height, atness,
and angle deviations. In addition, there is a reference to raw
measurement data, such as a point cloud in the
demonstrated case.
Due to the inherent characteristic of a digital model to have
the exact nominal geometry, an additional model was created
that has an imperfection of 0.01 m for demonstration purposes,
which was evenly applied to the length dimensions of the wall.
articial imperfection and corresponds to the actual state of the
component, which is compared with the nominal state in the
form of the model without imperfection in the course of quality
assurance. The Helios++ software was used to create the virtual
laser scan. The framework can generate 3-D point clouds based
on LiDAR (Light Detection And Ranging) simulations and
includes pre-congured models for various static and
dynamic platforms, such as tripod or quadrocopter, and
various scanner systems. The models can be extended to
implement additional platforms and systems (Bechtold and
e, 2016;Winiwarter et al., 2022). Figure 11 shows the
point cloud for the wall element.
By superimposing the point cloud and the 3D model of the
wall, which is stored in the AAS, deviations can be determined. In
this case, the distance between the actual geometry determined
from the 3D point cloud and the nominal geometry from the 3D
model was measured.
Web-Based GUI
A GUI was developed so that content can be retrieved from
the AAS and values, such as the measured deviations, can be fed
back into the AAS or corresponding services. The GUI is depicted
in Figure 12. To explain the GUI and its functionality, the
sequence diagram in Figure 13 is presented to show
the process behind the GUI. The quality control process
involves the user, the GUI, BaSyx with all AASs, the AAS of
the specicwallelement,theQuality Control submodel, and
validation service to compare the tolerances with the
measured values. If the user initializes the quality control for
Concrete Element #123, BaSyx is addressed via the GUI. In
BaSyx, the AAS of element #123 must then be used to transfer
FIGURE 10 | Quality Control Submodel.
FIGURE 11 | Virtual Laserscan.
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Kosse et al. Digital Twin Enabling Serial Construction
the tolerances for width, height, and depth stored in the Quality
Control submodel back to the GUI. These are displayed under
max. tolerances.To perform a quality check, the actual
deviations must also be entered in the new_valueeld in
the GUI. The entered values are then transmitted to the Quality
Control submodel and stored there. In the GUI, the entered
values are then displayed under the current value as soon as they
are stored in the AAS. To compare deviations and tolerances,
which can be triggered via the button in the GUI at quality
check, a validation service accesses the GUI and compares the
two values. The result, in this case, either within or out of
tolerance, is then transmitted to the GUI and displayed.
Case Study Conclusion
The case studies show that the concept of the AAS can
be implemented for both singular property collections such
as material, as well as the QC process of a concrete wall
element. An external application, in this case, the web-based
GUI, can access the contents of the AAS. The process results
canthenbestoredintheAASs concerning submodel. Even
though the use case is a relatively simple scenario where only a
be extended as needed for future applications.
Most precast concrete elements are currently produced in a
handcrafted manufacturing process. The basis for the
automation of these analog processes is digitization. Data that
accumulates during the manufacturing process must be collected,
processed, and analyzed to steer production processes and enable
adaptation and optimization of the production. Therefore, the
end-to-end digitalization and networking of all processes and
stakeholders in production are essential for automating the
production of precast concrete elements. For optimized
production of precast concrete elements, which have a high
degree of individualization, it is essential to implement smart
capabilities, such as networking and communication, so that the
components can independently nd their optimal path through
the factory and production processes. Current Building
Information Modeling (BIM) approaches provide all necessary
information and data of the elements themselves on a type-level
but are not suitable to independently exchange data with other
actors of the production process on an instance level. This paper
presents an approach to solve this problem based on developing a
digital twin for precast concrete elements, which can then be
manufactured in a fully networked and digitized production
process. The central aspect in the development of the digital
twin was to allow the twin to carry and exchange data and
information with other actors in the production system
independently and in a self-organized manner, in this case, in
the process step of quality control. Due to data processing,
communication, and interaction capabilities, the presented
approach extends existing BIM approaches after the detailed
design phase. It connects the information via the digital twin
to the production of precast concrete elements within cyber-
physical production systems with a higher degree of automation
and exibility.
This work developed a concept for implementing digital twins
based on the AAS concept developed by the German Platform
Industry 4.0 consortium. In terms of content, the AAS approach
comprises all the relevant data and information required by the
production system for production and quality assurance of
precast concrete elements. The modular structure enables the
AAS to be expanded with additional data for new production
machines, equipment, or sensors. The data stored in the AAS is
veriable and can be queried to check requirements enabling the
decision-making capability of the digital twin, which is essential
for the quality assurance process when checking dimensional
FIGURE 12 | Web-based GUI.
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Kosse et al. Digital Twin Enabling Serial Construction
tolerances, among other things. The AAS data is accessed either
via OPC UA or REST query. A special Industry 4.0 language
enables data exchange between AASs. The initial focus is put on
accessing the data via REST query.
Further research will address the implementation of the
Industry 4.0 language. Therefore, messages need to be dened
for communication between precast concrete elements and, for
example, laser scanners. The digital twin developed in our
research can transmit machine parameters to production
machines on the one hand and IoT data platforms, on the
other hand, to apply various analysis methods of machine
learning and articial intelligence. The digital twin presented
meets all data management requirements for future production
systems. Future developments in the AEC sector concerning
industrialized production are enabled through the exibility
and adaptability of this approach. All production steps can be
optimally networked, and the data can be made available in an
uncomplicated manner. In low or non-automated productions,
the digital twin is also advantageous. Content can be used through
handheld devices or computer vision technologies such as
augmented and virtual reality to support workers in their
work optimally. Production data can be stored in the digital
twin by shop oor workers with little effort so that analyses of the
production process and ultimately optimizations are also possible
here. At the same time, the digital twin is the ideal basis for future
automation efforts.
This work demonstrated a quality assurance process with a
laser scanner as a rst example. It was shown how a comparison
of the actual geometry based on a point cloud with dimensional
tolerances could be implemented for a wall element. The
measurement data storage and the comparison with the
tolerances were carried out manually for demonstration
purposes. Automation of this adjustment is possible and is
planned for further research. A prerequisite for automating the
quality assurance process is providing the laser scanner with an
AAS and implementing the query logic using the I4.0 language.
Semantic queries can be implemented using the RDF serialization
of the AAS via SPARQL or SHACL queries or by using the
ontologies developed in the concept section of this paper. Via this
ability to implement different logic, the precast concrete element
FIGURE 13 | Sequence diagram of the communication process in the GUI.
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Kosse et al. Digital Twin Enabling Serial Construction
can make intelligent decisions independently. This can include,
among other things, the component disqualifying itself when
specied requirements and tolerances are not met. To further
verify the possibility of using the presented concept in the
production of precast concrete elements, it is necessary to
represent other participants of the production system through
AASs, in particular, to show the interactions between each
participant. This research shows how data and information for
production and quality assurance can be structured using the
digital twin concept. Implementing the digital twin based on the
AAS enables advanced methods for communication with and
between digital twins. The developed concept will be driven
forward in further research projects to implement additional
interaction possibilities to realize a exible and adaptable
production in cyber-physical production systems.
This research was set out to develop a digital twin for the
production of precast concrete elements based on the
implementation of the digital twin in Industry 4.0the AAS. A
key strength of the proposed approach is its exibility and
extensibility so that the digital twin can represent any precast
concrete element that is either part of a production system
applying the methods and principles of Industry 4.0 or is
about to transform into one. Due to the various interaction
possibilities of the AAS, the precast concrete element is
intelligently able to participate in the production process.
The present research aimed to develop the rst template for an
AAS for precast concrete elements. For this purpose,
requirements were derived from a use case analysis, based on
which the data structure of the AAS was developed. The second
aim of this study was to develop a methodology for creating
AASs based on the BIM model of a modularized building. The
functionality of the digital twin was demonstrated using a virtual
precast wall element, for which quality control was performed
with a virtual laser scanner. The application shows how the data
can be made available uncomplicated and how the digital twin
enables the precast concrete element to make intelligent
The present work has some limitations. The administration
shell described in the concept/application section is a prototype
that has only been applied to the use case presented. Furthermore,
no communication between administration shells has been
implemented yet, as the administration shell of a precast
concrete element has been considered in isolation so far. The
data structure of the administration shell should be extended to
cover different precast element types and use cases.
Future research will address the extension of the concept to
the entire manufacturing plant, creating a digital factory for
precast concrete production. Modeling the interaction and data
ows along the production steps will eventually enable the
simulation and optimization of the entire production
process. For this concept to work, the digital twin of the
precast element must interact with other participants of the
production system, i.e., production or transport machines, to
exchange data independently. For this purpose, future research
is also concerned with implementing the Industry 4.0 language
for the application in the production of precast concrete
The original contributions presented in the study are included in
the article/Supplementary Material, further inquiries can be
directed to the corresponding author.
SK, OV, MW, MK and DG contributed to the design and
implementation of the research, to the analysis of the results
and to the writing of the manuscript.
This research is funded under Grant No. 423963709 by the
German Research Foundation as part of the priority program
SPP2187 - Adaptive modularized constructions made in ux.
We acknowledge support by the Open Access Publication Funds
of the Ruhr University Bochum.
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Main: ZVEI - Zentralverband Elektrotechnik und Elektronikindustrie e.V.
Conict of Interest: The authors declare that the research was conducted in the
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Frontiers in Built Environment | April 2022 | Volume 8 | Article 86472220
Kosse et al. Digital Twin Enabling Serial Construction
... For the industrialized production of precast concrete elements, these data represent a subset of the data requirements (cf. [8]). The data captured in the BIM model can be used to create digital twins, ensuring a continuous flow of data between the design phase and production and avoiding further fragmentation. ...
... The approach presented in this paper integrates an IFC-based BIM model into the digital twin for the industrialized production of precast concrete parts based on the administration shell of Industrie 4.0. The paper builds on the extensive preliminary work of [8], who developed data requirements and an administration shell template for precast concrete parts which are used as a basis for the presented approach. property sets which are then assigned to objects [9]. ...
... The prerequisite for the application is an IFC file and administration shell template, both being passed to the application as input. In this case study, the administration shell template developed by Kosse, Vogt, Wolf, et al. [8] is used. The application's functionality includes mapping material and reinforcement data and the addition of the reference to the underlying IFC file. ...
Conference Paper
Full-text available
The automation of production processes has made enormous progress in recent years through modern communication and information technologies. Through continuous intelligent networking of components, machines, and processes in Industry 4.0 (I4.0), the production of precast concrete elements can be carried out more efficiently in terms of both time and costs. The digital twin, as a central information container, is a fundamental concept in I4.0 that collects and provides relevant data and information across all lifecycle phases of a component. As an established tool for the planning and design phase, Building Information Modeling (BIM) focuses on designing and describing buildings and infrastructure. However, BIM models are not suitable for communication and interaction in cyber-physical systems and real-time data integration. The aim, therefore, must be to combine current BIM concepts with methods from I4.0, which enable self-organized and decentralized production. For this, suitable interfaces must be developed that ensure a continuous flow of information across all lifecycle phases. This paper presents an approach for digital twins' rapid and automated creation based on implementing the asset administration shell, a digital twin framework known from I4.0. In the presented approach, an administration shell template of a component, which already contains the necessary semantic data structures, is instantiated with a corresponding BIM model's design and construction data. The concept is demonstrated and validated using a virtual precast concrete column as an example. The proposed approach thus combines BIM with I4.0 methods and ensures a continuous flow of information between planning and production.
Digital twin for the automated production of precast concrete elements. Using precast concrete elements in construction has many advantages, such as cost and planning reliability, precision, and efficiency. In most cases, the production of precast concrete elements is not fully automated. There are many manual work steps that prevent a further increase in productivity. The basis for full automation is the use of information and communication technology. A digital twin as an integrated data model is necessary for this. This paper examines how the combination of Building Information Modeling with methods from the context of Industry 4.0, which enable largely self-organized and decentralized production, can provide the basis for the complete automation and networking of all production steps. Based on the administration shell, which implements the digital twin in Industry 4.0, a suitable description for digital twins of precast concrete modules is being developed by developing data and interaction models for industrialized production. , Ernst und Sohn. All rights reserved.
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The lack of integration between the digital and physical world results in a lower level of efficiency and collaboration in the construction industry. Digital twin technology, which creates a visual and digital model of a corresponding physical object for simulating, monitoring, analyzing, and other actions throughout the whole life cycle, is considered as an effective solution to address these problems. This research proposes a framework to utilize digital twins and extend the existing level of details (LoDs) of building information modeling (BIM) for construction site management. This study analyzes and improves the operation principle and mechanism of digital twins, including the digital representation based on BIM, Internet of Things (IoT), data storage, integration, and analytics, as well as interaction with the physical environment. Questionnaires and interview results verify that the proposed framework can support construction site monitoring and management, enhance quality and efficiency, and improve construction safety. It also acknowledges the contribution of LoDs' extension to construction site management. The interviews underline the main challenges that BIM, IoT, and data processes face in practical applications.
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The concept of digital twins is proposed as a new technology-led advancement to support the processes of the design, construction, and operation of built assets. Commonalities between the emerging definitions of digital twins describe them as digital or cyber environments that are bidirectionally-linked to their physical or real-life replica to enable simulation and data-centric decision making. Studies have started to investigate their role in the digitalization of asset delivery, including the management of built assets at different levels within the building and infrastructure sectors. However, questions persist regarding their actual applications and implementation challenges, including their integration with other digital technologies (i.e., building information modeling, virtual and augmented reality, Internet of Things, artificial intelligence, and cloud computing). Within the built environment context, this study seeks to analyze the definitions and characteristics of a digital twin, its interactions with other digital technologies used in built asset delivery and operation, and its applications and challenges. To achieve this aim, the research utilizes a thorough literature review and semi-structured interviews with ten industry experts. The literature review explores the merits and the relevance of digital twins relative to existing digital technologies and highlights potential applications and challenges for their implementation. The data from the semi-structured interviews are classified into five themes: definitions and enablers of digital twins, applications and benefits, implementation challenges, existing practical applications, and future development. The findings provide a point of departure for future research aimed at clarifying the relationship between digital twins and other digital technologies and their key implementation challenges.
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The key challenges of the building, engineering, construction, operations, and mining (BECOM) industries are the lack of trust, inefficiencies, and the fragmentation of the information value chain into vulnerable data silos throughout the lifecycle of projects. This paper aims to develop a novel conceptual model for the implementation of blockchain technology (BCT) for digital twin(s) (DT) in the BECOM industry 4.0 to improve trust, cyber security, efficiencies, information management, information sharing, and sustainability. A PESTELS approach is used to review the literature and identify the key challenges affecting BCT adoption for the BECOM industry 4.0. A review of the technical literature on BCT combined with the findings from PESTELS analysis permitted researchers to identify the key technological factors affecting BCT adoption in the industry. This allowed offering a technological framework—namely, the decentralized digital twin cycle (DDTC)—that leverages BCT to address the key technological factors and to ultimately enhance trust, security, decentralization, efficiency, traceability, and transparency of information throughout projects’ lifecycles. The study also identifies the gaps in the integration of BCT with key technologies of industry 4.0, including the internet of things (IoT), building information modeling (BIM), and DT. The framework offered addresses key technological factors and narrows key gaps around network governance, scalability, decentralization, interoperability, energy efficiency, computational requirements, and BCT integration with IoT, BIM, and DT throughout projects’ lifecycles. The model also considers the regulatory aspect and the environmental aspect, and the circular economy (CE). The theoretical framework provides key technological building blocks for industry practitioners to develop the DDTC concept further and implement it through experimental works. Finally, the paper provides an industry-specific analysis and technological approach facilitating BCT adoption through DT to address the key challenges and improve sustainability for the BECOM industry 4.0.
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As key technologies of the fourth industrial revolution, blockchain and digital twins have great potential to enhance collaboration, data sharing, efficiency, and sustainability in the construction industry. Blockchain can improve data integrity and enhance trust in the data value chain throughout the entire lifecycle of projects. This paper aims to develop a novel theoretical framework for the adoption of environmentally sustainable blockchain-based digital twins (BCDT) for Construction Industry (CI) 4.0. The paper identifies which key data from construction projects lifecycle should be anchored in BCDTs to benefit CI 4.0 and the environment. The paper also identifies key factors and non-functional requirements necessary for the adoption of BCDTs in a decentralized and sustainable CI 4.0. At first, a content analysis of the literature allowed the identification of which data from projects lifecycle would benefit from blockchain technology (BCT) adoption and what the key factors and non-functional requirements necessary for the adoption of BCDT in the CI4.0 are. Furthermore, the analysis of structured interviews and online survey permitted to firstly validate the hypotheses raised from the literature and to offer a novel framework for BCDT of CI 4.0 in the context of the circular economy (CE). The findings are that (1) the key project lifecycle data relevant for BCDTs relate to the BIM dimensions (3D, 4D, 5D, 6D, 7D, and 8D) and a new dimension called the contractual dimension (cD) is also proposed. (2) Ecosystems of BCDTs should embrace a novel form of collaboration that is decentralized and presented as Level 4 maturity for BCDTs. This new level of maturity leverages distributed blockchain networks to enhance collaboration, processes automation with smart contracts, and data sharing within a decentralized data value chain. Finally (3), the main non-functional requirements for BCDTs are security, privacy, interoperability, data ownership, data integrity, and the decentralization and scalability of data storage. With the proposed framework including the BCDT dimensions, the Maturity Level 4, and the key non-functional requirements, this paper provides the building blocks for industry practitioners to adopt BCDTs. This is promising for CI 4.0 to embrace a paradigm shift towards decentralized ecosystems of united BCDTs where trust, collaboration, data sharing, information security, efficiency, and sustainability are improved throughout the lifecycle of projects and within a decentralized CE (DCE).
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The construction industry faces multiple challenges, where transition to circular production is key. Digitalisation is a strategy to increase the sector’s productivity, competitiveness, and efficiency. However, digitalisation also impacts environmental goals, such as those concerning more eco-friendly solutions, energy efficiency, products recycling, and sustainability certifications. These strategies rely on data, understood as digital, interoperable, incremental and traceable. Data related concepts, such as digital data templates (DDT) and digital building logbooks (DBL), contribute to “good data”. Despite some research focused on each one, little importance has yet been given to their combination. Relevant relationships and overlaps exist, as they partially share the exact same data through the built environment life cycle. This research aims to provide improved understanding on the role of these concepts and their contribution to a more circular industry. The review develops conceptualisations where DDT and DBL are complementary and framed within an incremental digital twin construction (DTC). Misconceptions or confrontations between these three solutions can therefore stand down, for the benefit of a data-driven priority. To increase understanding and reduce misconceptions, our study developed the “Digital data-driven concept” (D3c). This concept contribution is the ability to structure, store, and trace data, opening way to streamlined digital transformation impacting circular built environment concerns.
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Topographic laser scanning is a remote sensing method to create detailed 3D point cloud representations of the Earth's surface. Since data acquisition is expensive, simulations can complement real data given certain premises are met: (i) models of 3D scene and scanner are available and (ii) modelling of the beam-scene interaction is simplified to a computationally feasible while physically realistic level. A number of laser scanning simulators for different purposes exist, which we enrich by presenting HELIOS++. HELIOS++ is an open-source simulation framework for terrestrial static, mobile, UAV-based and airborne laser scanning implemented in C++. The HELIOS++ concept provides a flexible solution for the trade-off between physical accuracy (realism) and computational complexity (runtime, memory footprint), as well as ease of use and of configuration. Features of HELIOS++ include the availability of Python bindings (pyhelios) for controlling simulations, and a range of model types for 3D scene representation. Such model types include meshes, digital terrain models, point clouds and partially transmissive voxels, which are especially useful in laser scanning simulations of vegetation. In a scene, object models of different types can be combined, so that representations spanning multiple spatial scales in different resolutions and levels of detail are possible. HELIOS++ follows a modular design, where the core components of platform, scene, and scanner can be individually interchanged, and easily configured. HELIOS++ further allows the simulation of beam divergence using a subsampling strategy, and is able to create full-waveform outputs as a basis for detailed analysis. We show how HELIOS++ positions among other VLS software in terms of input model support and simulation of beam divergence in a literature survey. We also perform a direct comparison of simulations with DART, where we employ a scene from the Radiative Transfer Model Intercomparison (RAMI). This example shows that HELIOS++ takes about 10 times longer than DART for parsing and preparing the 3D scene, but performs about 314,000 times faster in the beam simulation, achieving 200,000 rays/s. Comparing HELIOS++ to its predecessor, HELIOS, revealed reduced runtimes by up to 99%. Virtually scanned point clouds may be used for a broad range of applications as shown in literature. We could identify four main categories of use cases prevailing at present, which benefit from simulated LiDAR point clouds: data acquisition planning, method evaluation, method training and sensing experimentation. We conclude that a general-purpose LiDAR simulator can be employed for many different scientific applications, as long as it is ensured that the simulation adequately represents reality, which is specific to the given research question.
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Modular production systems provide the opportunity to react more flexibly to customer-individual requirements resulting in smaller batch sizes. The implementation of capabilities in production systems leads to a skill-based production which manifests on the modular level. Electrical energy consumption can be analyzed more in detail if the consumption can be linked to the respective skill. The aim of this paper is to show the conception of an energy consumption interface in a modular skill-based production environment with the Asset Administration Shell. The current state of the art of modular skill-based production and modular energy consumption measurement with Infrastructure Nodes and Asset Administration Shell is introduced. The role of the modular level for an energy consumption interface and conceptional approaches of this interface to access and collect consumption data is presented on this basis. Furthermore, the prototypical implementation of the energy related Asset Administration Shell submodel with the BaSyx-Middleware is presented.
de Auf dem Weg zur digitalen Brücke existieren bereits erste Lösungen, die den Lebenszyklus einer Brücke abbilden können. Für die Planung, den Bau und den Unterhalt stehen unterschiedlichste Werkzeuge, z. B. BIM, DIN 1076, SIB-Bauwerke, Monitoring etc. zur Verfügung, die jeweils mit unterschiedlichen Datenformaten arbeiten. Für ein intelligentes Erhaltungsmanagement müssen aber alle Daten mit den verschiedenen Datenformaten zusammengeführt, abgelegt und so verwaltet werden können, dass über den gesamten Lebenszyklus einer Brücke die Abbildung eines ganzheitlichen digitalen Zwillings eines Bauwerks möglich ist. Die Autoren haben dafür mit BBox den Prototyp einer Verwaltungsschale (VWS) auf Grundlage von Industrie 4.0 entwickelt. Damit wird das physikalisch-ingenieurtechnische Modell zur Zustandsbewertung der Brücke in den Mittelpunkt gestellt und der gesamte Lebenszyklus einer Brücke kann unabhängig von Datenformaten digital erfasst werden. Da der Aufbau der VWS durch die Granularität optimal strukturiert ist, bietet die Ablage und Einspeisung von Messdaten sowohl die Grundlage eines Live-Monitorings als auch den Grundstein für maschinelles Lernen (ML). Der Datenzugriff via S3-Schnittstelle erleichtert die Entwicklung von eigenen Prognosemodellen mit Informationsmustern (SHIP – Structural Health Information Pattern). Am Beispiel der Heinrichsbrücke Bamberg wird die praktische intelligente Umsetzung des Bauwerksmonitorings inkl. VWS mit Informationsmustern und ML gezeigt. Abstract en Digital twin: asset administration shell BBox as data storage over the life cycle of a bridge On the way to the digital bridge, initial solutions already exist that can map the life cycle of a bridge. A wide variety of tools are available for planning, construction and maintenance, e. g. BIM, DIN 1076, SIB structures, monitoring etc., each of which works with different data formats. For an intelligent maintenance management, however, all data with the different data formats must be merged, stored, and managed in such a way that the mapping of a holistic digital twin of a structure is possible over the entire life cycle of a bridge. For this purpose, the authors have developed BBox, a prototype of an asset administration shell (AAS) based on Industry 4.0. This places the physical-engineering model for assessing the condition of the bridge at the center, and the entire life cycle of a bridge can be digitally recorded independently of data formats. Since the structure of the AAS is optimally structured through granularity, the storage and feeding of measurement data provides both the basis of live monitoring and the cornerstone for machine learning (ML). The data access via S3 interface facilitates the development of own prognosis models with information patterns (SHIP – Structural Health Information Pattern). Using the Heinrichsbrücke Bamberg as an example, the practical intelligent implementation of structural monitoring incl. AAS with information patterns and ML is shown.
Conference Paper
Building Information Modeling (BIM) is increasingly used throughout the construction industry as a crucial tool for project management. In order to use BIM throughout the entire life cycle of a building, aspects of production, maintenance and renovation of the structure must not be neglected, especially if the share of industrial prefabrication with corresponding modularization increases. The challenge is also to digitally support new prefabrication and production concepts during the design and recycling process. In this paper, the authors present an approach to transfer digital twin concepts from the field of mechanical engineering to civil engineering and thus extend current BIM approaches by cyber physical components. As a use case, the production of pre-cast concrete modules made of free-form high performance concrete in the construction industry is presented and in particular a concept for the transfer and systematic collection of requirements, relevant information models and interactions that form an administrative shell for digital twins in BIM environments.